AI Engineer

Aptagrim Consulting; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Aptagrim Consulting is seeking an experienced AI Engineer to translate business problems into reliable AI solutions, from experimentation through production. This hands-on role requires strong engineering fundamentals, problem-solving and ownership of solution quality, performance and scalability.

You will collaborate with Product, Delivery, Engineering, QA and client-facing teams to deliver end-to-end AI systems, including generation of robust pipelines, model selection, and scalable

Qualifications

• Hands-on AI engineer with end-to-end delivery experience.
• Strong foundation in ML/DL, NLP and transformer models.
• Experience with production-grade Python backends and APIs.

Responsibilities

• Own end-to-end AI development from research to production deployment.
• Build ML/DL solutions with data prep, feature engineering, training and monitoring.
• Develop Generative AI apps using prompt engineering, embeddings and retrieval.
• Evaluate models on accuracy, latency, privacy, and cost.
• Engineer for production including concurrency, GPU usage and scaling.
• Implement MLOps/LLMOps with CI/CD and observability.
• Build backend services and APIs with frontend capabilities.
• Debug across data, models, prompts, APIs and infra.
• Collaborate with architecture, risks, and cross-team stakeholders.

Skills

Python

ML & DL

NLP & Transformers

PyTorch

TensorFlow

Hugging Face

LLMs

RAG

Embeddings

FastAPI

Django

SQL/PostgreSQL

Git

Docker

Linux

Tools

FastAPI

Flask

Django

SQL/PostgreSQL

Git

Docker

Linux

Job description

We are looking for an experienced AI Engineer who can independently understand business problems, select the right technical approach and build reliable AI solutions from experimentation through production.

This is a hands-on role requiring strong engineering fundamentals, systematic problem-solving and ownership of solution quality, performance and scalability. You will work closely with Product, Delivery, Engineering, QA and client-facing teams.

Key Responsibilities

• Own end-to-end AI development: Take solutions through research, Proof of Concept, development, testing, UAT, production deployment and ongoing improvement.
• Build ML and Deep Learning solutions: Develop pipelines covering data preparation, feature engineering, model selection, training, optimization, validation, monitoring and retraining.
• Develop Generative AI applications: Build RAG, knowledge retrieval and agentic solutions using prompt engineering, embeddings, vector databases, structured outputs and tool calling.
• Select and optimize models: Evaluate open-source and commercial LLMs, SLMs and classical ML models based on accuracy, latency, privacy, cost and business requirements. Apply fine-tuning and optimization techniques where appropriate.
• Validate AI quality: Create benchmark datasets, automated evaluations and regression tests to assess accuracy, relevance, groundedness, completeness, consistency and hallucination.
• Engineer for production: Address concurrency, batching, caching, asynchronous processing, GPU utilization, throughput, failure handling and infrastructure cost.
• Implement MLOps and LLMOps: Establish versioning, experiment tracking, CI/CD, controlled deployments, observability, drift detection and rollback practices.
• Build application services: Develop robust Python backend services, APIs and integrations, with sufficient frontend capability to independently demonstrate end-to-end AI prototypes.
• Investigate and resolve failures: Debug issues across data, models, prompts, retrieval, APIs, infrastructure and application logic, identifying and addressing root causes.
• Collaborate and contribute: Participate in architecture discussions, communicate risks and dependencies, support engineers and research emerging AI approaches.

Required Skills

• Strong Python proficiency and experience writing clean, maintainable, testable production code.
• Strong foundations in Machine Learning, Deep Learning, NLP and Transformers, with hands-on experience in PyTorch or TensorFlow and Hugging Face.
• Practical experience with LLMs, SLMs, RAG, embeddings, vector databases, prompt engineering and LLM evaluation.
• Understanding of model architectures, tokenization, attention, context management, inference behaviour and model limitations.
• Experience with model adaptation and optimization approaches such as LoRA, QLoRA, PEFT, instruction tuning, quantization or distillation.
• Backend development using FastAPI, Flask or Django, with REST APIs, databases, asynchronous processing, authentication, RBAC and third-party integrations.
• Working knowledge of SQL/PostgreSQL, Git, Docker and Linux.
• Ability to independently review, debug and take ownership of implementations, including code generated using AI-assisted tools.

Preferred Skills

• AI orchestration: LangGraph, LangChain or equivalent frameworks.
• Model serving and infrastructure: vLLM, Triton, TGI, TensorRT-LLM, GPU deployment, Kubernetes and cloud, private cloud or on-premise environments.
• MLOps and evaluation: MLflow, Kubeflow, Airflow, DVC, RAGAS, DeepEval, LangSmith, promptfoo or equivalent tools.
• Application development: Redis, Celery, WebSockets, React/Next.js, JavaScript/TypeScript and HTML/CSS.
• Exposure to Speech-to-Text, Conversational AI, Voice AI, Computer Vision, Recommendation Systems or Multimodal AI.

Experience & Working Approach

• Demonstrated ownership of AI solutions from PoC to production; experience supporting real production users is strongly preferred.
• Strong research, debugging and communication skills, with the ability to work independently and collaborate across teams.
• Candidates with different experience levels may be considered where they demonstrate exceptional technical depth and ownership.